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import os
cimport _kenlm
cdef bytes as_str(data):
if isinstance(data, bytes):
return data
elif isinstance(data, unicode):
return data.encode('utf8')
raise TypeError('Cannot convert %s to string' % type(data))
cdef class FullScoreReturn:
"""
Wrapper around FullScoreReturn.
Notes:
`prob` has been renamed to `log_prob`
`oov` has been added to flag whether the word is OOV
"""
cdef float log_prob
cdef int ngram_length
cdef bint oov
def __cinit__(self, log_prob, ngram_length, oov):
self.log_prob = log_prob
self.ngram_length = ngram_length
self.oov = oov
def __repr__(self):
return '{0}({1}, {2}, {3})'.format(self.__class__.__name__, repr(self.log_prob), repr(self.ngram_length), repr(self.oov))
property log_prob:
def __get__(self):
return self.log_prob
property ngram_length:
def __get__(self):
return self.ngram_length
property oov:
def __get__(self):
return self.oov
cdef class State:
"""
Wrapper around lm::ngram::State so that python code can make incremental queries.
Notes:
* rich comparisons
* hashable
"""
cdef _kenlm.State _c_state
def __richcmp__(State qa, State qb, int op):
r = qa._c_state.Compare(qb._c_state)
if op == 0: # <
return r < 0
elif op == 1: # <=
return r <= 0
elif op == 2: # ==
return r == 0
elif op == 3: # !=
return r != 0
elif op == 4: # >
return r > 0
else: # >=
return r >= 0
def __hash__(self):
return _kenlm.hash_value(self._c_state)
def __copy__(self):
ret = State()
ret._c_state = self._c_state
return ret
def __deepcopy__(self):
return self.__copy__()
class LoadMethod:
LAZY = _kenlm.LAZY
POPULATE_OR_LAZY = _kenlm.POPULATE_OR_LAZY
POPULATE_OR_READ = _kenlm.POPULATE_OR_READ
READ = _kenlm.READ
PARALLEL_READ = _kenlm.PARALLEL_READ
class ARPALoadComplain:
ALL = _kenlm.ALL
EXPENSIVE = _kenlm.EXPENSIVE
NONE = _kenlm.NONE
cdef class Config:
"""
Wrapper around lm::ngram::Config.
Pass this to Model's constructor to set configuration options.
"""
cdef _kenlm.Config _c_config
def __init__(self):
self._c_config = _kenlm.Config()
property load_method:
def __get__(self):
return self._c_config.load_method
def __set__(self, to):
self._c_config.load_method = to
property show_progress:
def __get__(self):
return self._c_config.show_progress
def __set__(self, to):
self._c_config.show_progress = to
property arpa_complain:
def __get__(self):
return self._c_config.arpa_complain
def __set__(self, to):
self._c_config.arpa_complain = to
cdef class Model:
"""
Wrapper around lm::ngram::Model.
"""
cdef _kenlm.Model* model
cdef public bytes path
cdef _kenlm.const_Vocabulary* vocab
def __init__(self, path, Config config = Config()):
"""
Load the language model.
:param path: path to an arpa file or a kenlm binary file.
:param config: configuration options (see lm/config.hh for documentation)
"""
self.path = os.path.abspath(as_str(path))
try:
self.model = _kenlm.LoadVirtual(self.path, config._c_config)
except RuntimeError as exception:
exception_message = str(exception).replace('\n', ' ')
raise IOError('Cannot read model \'{}\' ({})'.format(path, exception_message))\
from exception
self.vocab = &self.model.BaseVocabulary()
def __dealloc__(self):
del self.model
property order:
def __get__(self):
return self.model.Order()
def score(self, sentence, bos = True, eos = True):
"""
Return the log10 probability of a string. By default, the string is
treated as a sentence.
return log10 p(sentence </s> | <s>)
If you do not want to condition on the beginning of sentence, pass
bos = False
Never include <s> as part of the string. That would be predicting the
beginning of sentence. Language models are only supposed to condition
on it as context.
Similarly, the end of sentence token </s> can be omitted with
eos = False
Since language models explicitly predict </s>, it can be part of the
string.
Examples:
#Good: returns log10 p(this is a sentence . </s> | <s>)
model.score("this is a sentence .")
#Good: same as the above but more explicit
model.score("this is a sentence .", bos = True, eos = True)
#Bad: never include <s>
model.score("<s> this is a sentence")
#Bad: never include <s>, even if bos = False.
model.score("<s> this is a sentence", bos = False)
#Good: returns log10 p(a fragment)
model.score("a fragment", bos = False, eos = False)
#Good: returns log10 p(a fragment </s>)
model.score("a fragment", bos = False, eos = True)
#Ok, but bad practice: returns log10 p(a fragment </s>)
#Unlike <s>, the end of sentence token </s> can appear explicitly.
model.score("a fragment </s>", bos = False, eos = False)
"""
if bos and eos:
return _kenlm.ScoreSentence(self.model, as_str(sentence))
cdef list words = as_str(sentence).split()
cdef _kenlm.State state
if bos:
self.model.BeginSentenceWrite(&state)
else:
self.model.NullContextWrite(&state)
cdef _kenlm.State out_state
cdef float total = 0
for word in words:
total += self.model.BaseScore(&state, self.vocab.Index(word), &out_state)
state = out_state
if eos:
total += self.model.BaseScore(&state, self.vocab.EndSentence(), &out_state)
return total
def perplexity(self, sentence):
"""
Compute perplexity of a sentence.
@param sentence One full sentence to score. Do not include <s> or </s>.
"""
words = len(as_str(sentence).split()) + 1 # For </s>
return 10.0**(-self.score(sentence) / words)
def full_scores(self, sentence, bos = True, eos = True):
"""
full_scores(sentence, bos = True, eos = Ture) -> generate full scores (prob, ngram length, oov)
@param sentence is a string (do not use boundary symbols)
@param bos should kenlm add a bos state
@param eos should kenlm add an eos state
"""
cdef list words = as_str(sentence).split()
cdef _kenlm.State state
if bos:
self.model.BeginSentenceWrite(&state)
else:
self.model.NullContextWrite(&state)
cdef _kenlm.State out_state
cdef _kenlm.FullScoreReturn ret
cdef float total = 0
cdef _kenlm.WordIndex wid
for word in words:
wid = self.vocab.Index(word)
ret = self.model.BaseFullScore(&state, wid, &out_state)
yield (ret.prob, ret.ngram_length, wid == 0)
state = out_state
if eos:
ret = self.model.BaseFullScore(&state,
self.vocab.EndSentence(), &out_state)
yield (ret.prob, ret.ngram_length, False)
def BeginSentenceWrite(self, State state):
"""Change the given state to a BOS state."""
self.model.BeginSentenceWrite(&state._c_state)
def NullContextWrite(self, State state):
"""Change the given state to a NULL state."""
self.model.NullContextWrite(&state._c_state)
def BaseScore(self, State in_state, str word, State out_state):
"""
Return p(word|in_state) and update the output state.
Wrapper around model.BaseScore(in_state, Index(word), out_state)
:param word: the suffix
:param state: the context (defaults to NullContext)
:returns: p(word|state)
"""
cdef float total = self.model.BaseScore(&in_state._c_state, self.vocab.Index(as_str(word)), &out_state._c_state)
return total
def BaseFullScore(self, State in_state, str word, State out_state):
"""
Wrapper around model.BaseScore(in_state, Index(word), out_state)
:param word: the suffix
:param state: the context (defaults to NullContext)
:returns: FullScoreReturn(word|state)
"""
cdef _kenlm.WordIndex wid = self.vocab.Index(as_str(word))
cdef _kenlm.FullScoreReturn ret = self.model.BaseFullScore(&in_state._c_state, wid, &out_state._c_state)
return FullScoreReturn(ret.prob, ret.ngram_length, wid == 0)
def __contains__(self, word):
cdef bytes w = as_str(word)
return (self.vocab.Index(w) != 0)
def __repr__(self):
return '<Model from {0}>'.format(os.path.basename(self.path))
def __reduce__(self):
return (Model, (self.path,))
class LanguageModel(Model):
"""Backwards compatability stub. Use Model."""
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